The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Machine learning systems, such as neural network based systems, typically rely on training data to be effective. An example system that implements a neural network may ingest information indicating outcomes for particular inputs. This ingested information may be training data, and the neural network can be trained based on the information. Subsequent to training the neural network, the example system can receive real data and determine outputs associated with the real data. For example, a neural network can be trained to identify particular features in images. Other machine learning systems may utilize training data to generate a model enabling the systems to produce sufficiently accurate predictions in newly received data.
Obtaining such training data can present technical difficulties, and ensuring the accuracy of such training data can be difficult. For example, training a machine learning system to precisely label particular features can require training images with accurate feature labels. The accuracy of these feature labels can be dependent upon a reviewing user, or a system automatically generating labels, and therefore the accuracy of the machine learning system can be lower with lesser accurate training images.